Interpretive Summary: Near-isogenic lines (NILs) are plant lines that have been developed to be genetically identical except for a few chromosomal segments which can be used to help discover genes that control agronomically important traits. Genetic methods used previously to identify which regions are different between a pair of NILs have been time consuming and may not identify all the regions that are different. We compared three new methods which could be faster and more accurate for mapping the regions which are different between NILs. It was found that all three new methods are complementary when utilized in combination and are very efficient at identifying regions that are different between a pair of NILs. These new methods will be used by geneticists and plant breeders to quickly identify regions different between NILs to help identify genes may be controlling agronomically important traits.

Technical Abstract:
Near-Isogenic Lines (NILs) are valuable genetic resources for many crop species, including soybean. The development of new molecular platforms promises to accelerate the mapping of genetic introgressions in these materials. Here we compare some existing and emerging methodologies for genetic introgression mapping: single-feature polymorphism analysis, Illumina Goldengate SNP genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data. We used these methods to map the introgressed regions in an iron-inefficient soybean NIL and found that the three mapping approaches are complimentary when utilized in combination. The comparative RNA-Seq approach offers several additional advantages, including the greatest mapping resolution, marker depth and de novo marker utility for downstream fine-mapping analysis. We applied the comparative RNA-Seq method to map genetic introgressions in an additional pair of NILs, exhibiting differential seed protein content. Furthermore, we attempted to optimize the comparative RNA-Seq approach by assessing the impact of sequence depth, SNP identification methodology, and post-hoc analyses on SNP discovery rates. We conclude that the comparative RNA-Seq approach can be optimized with sufficient sampling and by utilizing a post-hoc correction accounting for gene density variation that controls for false-discoveries.